Inflection AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Inflection AI AI

Fable surpasses GPT 5.5 completely

I've had several issues lately with GPT 5.5 xhigh where it would not be able to complete features, constantly making mistakes, one refactor work took two weeks. Well guess what Fable just completed it in 9 hours. I'm completely amazed at its capability. I've had it also build entire features in one shot. I've always expected that from OpenAI but always got a half finished feature. UI isn't proper, weird silly edge cases, and taking many many prompts and tests. Fable appears to read the user's intent extremely well its kind of scary. If this is the new inflection point going forward then I really think software engineer profession as a whole is going to look very different. Taste used to be the defensible moat but Fable has given me a glimpse of how fragile that imagination was. I've been a long time Codex $200 /month user and I finally upgrade to Claude Max because of how much superior Fable was. Only thing getting used to do is the usage limits but honestly because Fable is able to get things done faster and flawlessly it's something I could work with. Now if Anthropic could extend Fable or do some 2x promo like codex did , it could win a lot of us over. submitted by /u/Just_Lingonberry_352 [link] [comments]

Not "Is AI a bubble" but what kind of bubble. There's a difference, and it matters a lot.

I've been reading Boom by Byrne Hobart and Tobias Huber (Ben Thompson did a long interview with Hobart on Stratechery (if you want the audio version of the argument) and it reframed how I think about the current AI spending wave. The book splits bubbles into two types: Mean-reversion bubbles money piles into something that already exists, prices detach from reality, crash, nothing left behind. Housing 2008. Tulips. The crater kind. Inflection bubbles money piles into something that bets the world works differently going forward. Amazon wasn't a better bookstore. It was a categorically new thing. The investors looked insane by the standards of 1997. They were right about 2010. The dot-com crash is the cleanest example of an inflection bubble working as intended. Telecom companies borrowed insane amounts and laid fiber optic cable nobody needed. Then they went bankrupt. But the cable stayed. And because bankrupt companies built it, the internet was essentially free. The bubble funded the future and then got out of the way. So here's the actual question about AI: Google, Amazon, Microsoft, and Meta are on track to spend close to $700 billion on AI infrastructure in 2026 nearly double last year. That gap between what's being spent and what's being earned is real and large. But Hobart and Huber's deeper argument is that stagnation is more dangerous than a bubble. Progress has been quietly slowing since the 70s breakthroughs are rarer, more expensive, harder. Bubbles are sometimes the only force strong enough to override the collective risk aversion that stops necessary things from being built. The honest question isn't whether AI is a bubble. It probably is. The question is which type. Does AI produce something categorically new or is it a faster, more expensive version of software we already had? If it's the former, the infrastructure survives the crash and becomes the foundation for whatever comes next, the way fiber became the internet. If it's the latter, we get the crater. History only tells you which kind it was after the fact. What do you think inflection or mean-reversion? And what would actually convince you either way? submitted by /u/Relevant-Can1656 [link] [comments]

List of people at big-tech / professors / researchers who've jumped shit to launch their own AI labs for something Frontier/Foundational/AGI/Superintelligence/WorldModel

Note: gemini deep research -> rearranged/filtered ; valuation numbers likely not accurate but big point is quite mind blowing the number of researchers now with their own >100million/billion dolar values labs in quite a short time with a vague pitch and a maybe demo. Skipped perplexity/cursor/huggingface since they are with utility. Left some just for completion like black forest labs, synthesia, mistral since they have tanginble products. Skipped labs from china since they've been meaningfully killing it with their open source releases ───────────────────────────────────────────────────────── Safe Superintelligence Inc. (SSI) Founders:Ilya Sutskever (former OpenAI Chief Scientist), Daniel Gross, Daniel Levy Location & Founded:Palo Alto, USA & Tel Aviv, Israel | Founded: 2024 Funding / Valuation:$3B raised | Series A Description:Singularly focused on safely developing superintelligent AI that surpasses human capabilities. Deliberately avoids near-term commercial products to concentrate entirely on the technical challenge of safe superintelligence. ───────────────────────────────────────────────────────── Thinking Machine Labs Founders:Mira Murati (former OpenAI CTO), Barrett Zoph et al. Location & Founded:San Francisco, USA | Founded: 2025 Funding / Valuation:$2B seed | $12B valuation Description:Advance AI research and products that are customizable, capable, and safe for broad human-AI collaboration. Focused on frontier multimodal models with a strong safety and interpretability research agenda. ───────────────────────────────────────────────────────── Mistral AI Founders:Arthur Mensch, Guillaume Lample, Timothée Lacroix (former DeepMind & Meta FAIR) Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:~€11.7B valuation | Series C Description:Develops open-weight and proprietary frontier language and multimodal foundation models. Champions openness and efficiency in AI development, with models like Mistral 7B and Mixtral widely adopted in enterprise and research settings. ───────────────────────────────────────────────────────── Advanced Machine Intelligence (AMI) Founders:Yann LeCun (Meta Chief AI Scientist), Alexandre LeBrun, Laurent Solly Location & Founded:Paris, France | Founded: 2026 Funding / Valuation:$3.5B pre-money valuation | Seed Description:Aims to build world-model AI systems capable of reasoning, planning, and operating safely in real-world environments — directly inspired by LeCun's 'world model' thesis as an alternative path to AGI beyond current LLM paradigms. ───────────────────────────────────────────────────────── World Labs Founders:Fei-Fei Li (Stanford AI Lab), Justin Johnson et al. Location & Founded:San Francisco, USA | Founded: 2023 Funding / Valuation:$230M raised | Series D Description:Build AI models that can perceive, generate, reason, and interact with 3D spatial worlds. Focused on large world models (LWMs) that go beyond language and flat images to understand physical space and context. ───────────────────────────────────────────────────────── Eureka Labs Founders:Andrej Karpathy (former Tesla AI Director & OpenAI co-founder) Location & Founded:Tel Aviv, Israel & Kraków, Poland | Founded: 2024 Funding / Valuation:$6.7M seed Description:Creating an AI-native educational platform integrating AI Teaching Assistants to radically scale personalised learning. Envisions a future where an AI teacher can guide anyone through any subject, starting with deep technical topics like neural networks. ───────────────────────────────────────────────────────── H Company Founders:Former DeepMind researchers Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:€175.5M raised Description:Develops AI models to boost worker productivity through advanced agentic capabilities, with a long-term vision of achieving AGI. Focuses on models that can take sequences of actions and interact with digital environments. ───────────────────────────────────────────────────────── Poolside Founders:Jason Warner, Eiso Kant Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:$500M | Series B Description:Building AI agents that autonomously generate production-grade code, framed as a stepping stone toward AGI. Believes that software engineering is a key domain for training and demonstrating general reasoning capabilities. ───────────────────────────────────────────────────────── CuspAI Founders:Max Welling (University of Amsterdam / Microsoft Research), Chad Edwards Location & Founded:Cambridge, UK | Founded: 2024 Funding / Valuation:$130M raised | Series A Description:Accelerating materials discovery using AI foundation models, aiming to power human progress through AI-driven science. Applies large generative models to the design and prediction of novel materials for energy, medicine, and manufacturing. ───────────────────────────────────────────────────────── Inception Founders:Stefano Ermon (Stanford) Locat

I built a Steam game in 10 days with Claude Code — not a single line of code was written by me. Here's what was hard.

Hi everyone. I was curious how far Claude Code and Vibe Coding could actually go in a real production environment, so I just went for it. 10 days later, the game passed Steam's store review. Let me be clear upfront: I did not personally type a single line of code in this entire project. But "not coding" definitely did NOT mean "easy." In this first post, I want to share the technical shocks and limitations I ran into first. "I didn't code, but designing the logic was twice as hard" Not having to worry about syntax meant the speed was insane. But when the AI misunderstood my intent, it would spit out completely wrong code. In the end, I spent more time "explaining the logic I wanted" than I would have spent actually writing code. I realized that "explaining" is a higher-level task than "writing code" — and I felt that in my bones. The infinite loop of error logs When AI-written code throws errors, you're basically blind if you don't understand the code yourself. My approach was to throw the entire error log back at Claude and say "analyze why YOUR code is conflicting with YOUR other code." This "debugging back-and-forth" probably ate up half of the 10 days. The result: Steam store approval In the end, a game where no human wrote a single line of code passed the technical review of Steam — one of the biggest gaming platforms in the world. Setting aside the quality or fun factor of the game, I'm now convinced that the way games are made has reached a real inflection point. Is the age of "coders" ending and the age of "directors" beginning? I'm curious what you all think about AI-driven development like this. [Next posts preview] Post 2: I'll share the specific workflow — how I actually gave commands and worked with Claude Code and Unity in practice. Post 3: I'll break down how a game built without coding passed Steam's technical review on the first try. submitted by /u/New_Consequence3669 [link] [comments]

I used Claude to build a full-stack local business platform from scratch. Here’s what happened in 30 days of Google Search Console data

I’m a technical founder but not a full-stack engineer. My wife Kelsey and I had a vision for LocalSquare, a digital bulletin board where local businesses pin a visual ad to their town’s board for $1/month. 43,000+ ZIP codes, all 50 states, SSR pages with structured data, AI search optimization. Way more than I could build alone. Claude built virtually all of it. The stack: Node.js/Express monolith (~6,500 lines of server.js), sql.js (SQLite in-memory), Tailwind CSS, Stripe payments, Google/Facebook/Apple OAuth. Hosted on Render for $25/month. No React, no build step, no framework bloat. Every page is server-side rendered so search engines and AI models can read it without executing JavaScript. What Claude specifically built or architected: ∙ Full blog system with markdown rendering, scheduled publishing, RSS feed, auto-generated FAQ schema detection, and IndexNow integration ∙ JSON-LD structured data across every page type (Place, LocalBusiness, WebPage, BreadcrumbList, BlogPosting, FAQPage, Event, EducationalOrganization) ∙ Directory pages with pagination, weather, census data, air quality, events, school information, and nearby communities ∙ “Best in Town” category pages with dynamic SEO ∙ Event schema enrichment pulling from Ticketmaster API (endDate, performer, price, organizer, all the fields Google wants) ∙ Pin pages with full LocalBusiness schema and geo-targeting ∙ MCP server so AI agents can query our listings directly ∙ Sitemap generation with deduplication and safety nets for a database contamination issue we had (40K+ rows with bad URL slugs) ∙ LCP optimization (image preloading, fetchpriority, lazy loading) ∙ Blog share buttons, author schema with sameAs links for E-E-A-T signals ∙ Title tag truncation, canonical URL fixes, state page pagination to fix Bing and Google Search Console errors The workflow was: I upload server.js, describe what I need, Claude delivers a complete ready-to-deploy file. No patches, no “add this on line 347.” Full file, every time. I git push and it’s live. The biggest technical challenge was the in-memory database architecture. sql.js loads the entire SQLite database into RAM on startup. External scripts can’t write to it. Every data migration has to run through admin endpoints inside the running server process. Claude understood this constraint immediately and never once suggested an approach that would break it. The results after rebuilding SEO with Claude in February: ∙ 69,500 Google impressions (from near zero) ∙ 302 clicks in 28 days ∙ 666 organic keywords ranking ∙ Pages ranking across 12+ states ∙ Google sent us a 300-click milestone badge ∙ Zero dollars spent on ads The SEO/AEO/GEO work Claude did was the inflection point. Structured data on every page, proper schema markup, server-side rendering, AI-readable HTML, blog content targeting the exact queries people ask AI assistants. All of it compounding. I genuinely could not have built this without Claude. Not at this speed, not at this quality. It’s not just code generation. It’s architectural decisions, SEO expertise, understanding constraints, and delivering production-ready code for a live platform with real users and real revenue. yourlocalsquare.com/get-started if anyone wants to see it live.​​​​​​​​​​​​​​​​ submitted by /u/Inside_Raisin_745 [link] [comments]